Learning to rank: combinação de algoritmos aplicando stacking e análise dos resultados

• Ano de defesa: 2017
• Autor(a) principal: Paris, Bruno Mendonça
• Orientador(a): Omar, Nizam
• Banca de defesa: Silva, Leandro Augusto da , Fernandes, Clovis Torres
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Presbiteriana Mackenzie
• Programa de Pós-Graduação: Engenharia Elétrica
• Departamento: Faculdade de Computação e Informática (FCI)
• País: Brasil
• Palavras-chave em Português: recuperação de informação; ranking; learning to rank; stacking
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://dspace.mackenzie.br/handle/10899/24468

With the growth of the amount of information available in recent years, which will continue to grow due to the increase in users, devices and information shared over the internet, accessing the desired information should be done in a quick way so it is not spent too much time looking for what you want. A search in engines like Google, Yahoo, Bing is expected that the rst results bring the desired information. An area that aims to bring relevant documents to the user is known as Information Retrieval and can be aided by Learning to Rank algorithms, which applies machine learning to try to bring important documents to users in the best possible ordering. This work aims to verify a way to get an even better ordering of documents, using a technique of combining algorithms known as Stacking. To do so, it will used the RankLib tool, part of Lemur Project, developed in the Java language that contains several Learning to Rank algorithms, and the datasets from a base maintained by Microsoft Research Group known as LETOR.